Behaviour Based Data Dispatcher
Mohan Sai S1, Mona Teja K2, Akhil Amudala3, Sai Karthik M4, Swarnalatha P5
1Mohan Sai S, UG Student, School of Computer and Engineering, VIT Vellore, India.
2Mona Teja K, UG Student, School of Computer and Engineering, VIT Vellore, India.
3Akhil Amudala, UG Student, School of Computer and Engineering, VIT Vellore, India.
4Sai Karthik M, UG Student, School of Computer and Engineering, VIT Vellore, India.
5Swarnalatha P, Associate professor, School of Computer and Engineering, VIT Vellore, India.
Manuscript received on November 17., 2019. | Revised Manuscript received on November 24 2019. | Manuscript published on 30 November, 2019. | PP: 12940-12944 | Volume-8 Issue-4, November 2019. | Retrieval Number: D8428118419/2019©BEIESP | DOI: 10.35940/ijrte.D8428.118419
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Human life is a complex social structure. It is not possible for the humans to navigate without reading the other persons. They do it by identifying the faces. The state of response can be decided based on the mood of the opposite person. Whereas a person’s mood can be figured out by observing his emotion (Facial Gesture). The aim of the project is to construct a “Facial emotion Recognition” model using DCNN (Deep convolutional neural network) in real time. The model is constructed using DCNN as it is proven that DCNN work with greater accuracy than CNN (convolutional neural network). The facial expression of humans is very dynamic in nature it changes in split seconds whether it may be Happy, Sad, Angry, Fear, Surprise, Disgust and Neutral etc. This project is to predict the emotion of the person in real time. Our brains have neural networks which are responsible for all kinds of thinking (decision making, understanding). This model tries to develop these decisions making and classification skills by training the machine. It can classify and predict the multiple faces and different emotions at the very same time. In order to obtain higher accuracy, we take the models which are trained over thousands of datasets.
Keywords: Deep Convolution Neural Networks, Tensor Flow, Vgg16, Mobile Net, CV2, Haar Cascades.
Scope of the Article: Deep Learning.